Reference

Public API of tidysdmx — fetch SDMX schemas from FMR, infer schemas from tidy DataFrames, build structure maps from Excel templates, map data to dissemination schemas, validate datasets, and emit SDMX-ML artefacts ready for FMR upload.

FMR & Schemas

Fetch and parse SDMX schemas from a Fusion Metadata Registry.

fetch_schema(): Fetch the schema of a specified artefact from an SDMX registry.
fetch_dsd_schema(): Fetch a DSD schema from a Fusion Metadata Registry (FMR).
parse_artefact_id(): Parse an artefact identifier into its components: agency, id and version.
parse_dsd_id(): Parse a DSD identifier into its components.
create_schema_from_table(): Create a DSD, ConceptScheme, and Codelists from a DataFrame.

Structure Maps

Build, parse, validate, and write SDMX structure maps.

parse_mapping_template_wb(): Read an Excel mapping template and return all sheets as DataFrames.
build_structure_map_from_template_wb(): Build a complete StructureMap object by parsing a WB-format Excel template.
build_fixed_map(): Build a pysdmx FixedValueMap for setting a component to a fixed value.
build_implicit_component_map(): Build a pysdmx ImplicitComponentMap for implicit mapping rules.
build_date_pattern_map(): Build a DatePatternMap object for mapping date patterns between SDMX components.
build_value_map(): Create a pysdmx ValueMap object mapping a source value to a target value.
build_value_map_list(): Build a list of ValueMap objects from a pandas DataFrame, optionally including validity periods.
build_multi_value_map_list(): Build a list of MultiValueMap objects from a pandas DataFrame.
build_representation_map(): Build a RepresentationMap object from a pandas DataFrame using build_value_map_list.
build_multi_representation_map(): Build a MultiRepresentationMap object from a pandas DataFrame.
build_single_component_map(): Build a ComponentMap mapping one source component to one target component using a RepresentationMap built from a pandas DataFrame.
collect_structure_map_artifacts(): Collect the StructureMap and all its dependent RepresentationMaps.
validate_structure_map_references(): Validate that all RepresentationMap references are resolved.
prepare_structure_map_for_upload(): Prepare a StructureMap for upload by collecting all dependencies.

Mapping

Apply structure maps to tidy DataFrames.

map_structures(): Apply all mapping components from a StructureMap to a DataFrame.
apply_fixed_value_maps(): Apply FixedValueMap rules to a DataFrame.
apply_implicit_component_maps(): Apply ImplicitComponentMap rules to a DataFrame.
apply_multi_component_map(): Apply a single MultiComponentMap with regex support, preserving rule order.
map_to_sdmx(): Map DataFrame columns to SDMX values using a lookup mapping.
transform_source_to_target(): Transform a raw DataFrame into the format defined by a components map.

Standardisation

Prepare a mapped DataFrame for SDMX upload.

standardize_output(): Standardize the output DataFrame by adding SDMX reference columns.
standardize_sdmx(): Standardize a DataFrame by applying column and value transformations.
standardize_data_for_upload(): Standardize a DataFrame for SDMX upload.
standardize_indicator_id(): Fix the INDICATOR column to be uppercase and prefixed with dataset ID.
sanitize_variable(): Sanitize a raw string value into a valid SDMX code ID.
add_sdmx_reference_cols(): Add SDMX reference columns to a DataFrame.

Validation

Validate datasets against schemas and codelists.

validate_dataset_local(): Validate that a DataFrame is SDMX compliant and return a DataFrame of errors.
validate_columns(): Validate that all DataFrame columns are valid components or SDMX references.
validate_mandatory_columns(): Validate that all mandatory columns are present in the DataFrame.
validate_codelist_ids(): Validate that all values in coded columns are within the allowed codelist IDs.
validate_duplicates(): Validate that there are no duplicate rows for a given set of key columns.
validate_no_missing_values(): Validate that there are no missing values in mandatory columns.

Tidy Raw

Filter and shape raw inputs.

filter_tidy_raw(): Filter an SDMX DataFrame by removing rows that violate codelist constraints.
filter_rows(): Filter out rows where values are not in the allowed codelist.

Utilities

Helpers for codelists, components, Excel templates, and XML.

extract_validation_info(): Extract validation information from a given schema.
get_codelist_ids(): Retrieve all codelist IDs for given coded components.
extract_component_ids(): Retrieve all component IDs from a given pysdmx Schema.
create_mapping_rules(): Create Excel hyperlink formulas for components with representation maps.
build_excel_workbook(): Build a Workbook with component mapping and representation map sheets.
write_excel_mapping_template(): Generate an Excel mapping template with component and representation tabs.
read_mapping(): Read a JSON mapping file and parse its content into DataFrames.
fix_sdmx_xml_datatype_tags(): Fix incorrect SourceCodelist/TargetCodelist tags in SDMX-ML.
gen_urn(): Generate a full SDMX URN for any maintainable artefact.

QA

Quality-assurance helpers.

qa_coerce_numeric(): Coerce specified columns to numeric, removing rows with invalid values.
qa_remove_duplicates(): Remove duplicate rows from a DataFrame.

Kedro Integration

Kedro pipeline node wrappers.

kd_read_mappings(): Fetch multiple mappings from different files.
kd_standardize_sdmx(): Standardize a partitioned dataset into SDMX format.
kd_validate_dataset_local(): Validate a single DataFrame for SDMX compliance.
kd_validate_datasets_local(): Validate multiple datasets for SDMX compliance.

Lookups

Vectorised lookup helpers.

vectorized_lookup_ordered_v1(): Apply ordered regex matching to a Pandas Series.
vectorized_lookup_ordered_v2(): Apply ordered matching (regex or exact) to a Pandas Series.